预览加载中，请您耐心等待几秒...

基于AE和Transformer的运动想象脑电信号分类研究.docx

预览
1/3
2/3
3/3

在线预览结束，喜欢就下载吧，查找使用更方便

下载提示 文本预览

如果您无法下载资料，请参考说明：

1、部分资料下载需要金币，请确保您的账户上有足够的金币

2、已购买过的文档，再次下载不重复扣费

3、资料包下载后请先用软件解压，在使用对应软件打开

基于AE和Transformer的运动想象脑电信号分类研究 Abstract TheclassificationofmotorimageryEEGsignalshasbecomeanimportantresearchtopicinthefieldofbrain-computerinterfaces(BCIs)duetoitspotentialapplicationsinprostheticsandrehabilitation.Inrecentyears,deeplearningmodelssuchastheautoencoder(AE)andtransformerhaveshownremarkableperformanceinEEGsignalprocessingandclassification.Inthispaper,weproposeanovelapproachthatcombinesAEandtransformermodelsformotorimageryEEGsignalclassification.OurproposedmodelachievedanaccuracyofXX%onapubliclyavailabledataset,outperformingthestate-of-the-artmethods.TheresultssuggestthattheproposedapproachiseffectiveformotorimageryEEGsignalclassificationandcancontributetothedevelopmentofBCIs. Introduction BCIsareintendedtoestablishadirectcommunicationpathwaybetweenthebrainandexternaldeviceswithouttheneedformotorexecution.ThemotorimageryEEGsignalsgeneratedduringtheimaginationofaspecificlimbmovementprovideanidealinputsourceforBCIs,facilitatingthecontrolofexternaldevices.MotorimageryEEGsignalclassificationisthekeystepinthedevelopmentofBCIs. OneofthemainchallengesinmotorimageryEEGsignalclassificationisthehighinter-subjectvariabilityandnoiseintheEEGsignals.Inrecentyears,deeplearningmodelssuchasAEandtransformerhaveshownremarkableperformanceinEEGsignalprocessingandclassification. Inthispaper,weproposeanovelapproachthatcombinesAEandtransformermodelsformotorimageryEEGsignalclassification.TheproposedapproachusestheAEtoextractrelevantfeaturesfromtheEEGsignalsandthetransformerforthefinalclassification.Theeffectivenessoftheproposedapproachisevaluatedonapubliclyavailabledataset. RelatedWork Inrecentyears,variousmachinelearningmodelshavebeenproposedforEEGsignalprocessingandclassification.Supportvectormachines(SVMs)andartificialneuralnetworks(ANNs)havebeenwidelyusedforEEGsignalclassification.However,deeplearningmodelssuchasconvolutionalneuralnetworks(CNNs)andrecurrentneuralnetworks(RNNs)haveshownbetterperformanceduetotheirabilitytoautomaticallyextractrelevantfeaturesfromthedata. AEisat